Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/16608
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dc.contributor.authorVatsa, Amit Kumar
dc.contributor.authorJayaswal, Sachin
dc.date.accessioned2015-11-10T06:09:53Z
dc.date.available2015-11-10T06:09:53Z
dc.date.copyright2015
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/11718/16608
dc.description.abstractFacility location problems reported in the literature generally assume the problem parameter values (like cost, budget, etc.) to be known with complete certainty, even if they change over time (as in multi-period versions). However, in reality, there may be some uncertainty about the exact values of these parameters. Specifically, in the context of locating primary health centers (PHCs) in developing countries, there is generally a high level of uncertainty in the availability of servers (doctors) joining the facilities in different time periods. For transparency and efficient assignment of the doctors to PHCs, it is desirable to decide the facility opening sequence (assigning doctors to unmanned PHCs) at the start of the planning horizon. For, this we present a new formulation for a multi-period maximal coverage location problem with server uncertainty (MMCLPSU). We further demonstrate the superiority of our proposed formulation over the only other formulation reported in the literature. For instances of practical size, we provide Benders' decomposition based solution method, along with several refinements. For instances that CPLEX MIP solver could solve within a time limit of 20 hours, our proposed solution method turns out to be of the order of 150 - 250 times faster for the problems with complete coverage, and around 1000 times faster for gradual coverage.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management Ahmedabaden_US
dc.subjectFacility Locationen_US
dc.subjectPrimary Health Centersen_US
dc.subjectBenders' Decompositionen_US
dc.titleA new formulation and benders' decomposition for multi-period facility location problem with server uncertaintyen_US
dc.typeWorking Paperen_US
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